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978-3-8439-4250-8, Reihe Produktentwicklung
Vahid Ghaffari Mejlej
Adaptive search approach in the multidisciplinary optimization of lightweight structures using hybrid metaheuristics
220 Seiten, Dissertation Technische Universität Braunschweig (2019), Softcover, A5
Within the last few years environmental regulations, safety requirements and market competitions forced the automotive industry to open up a wide range of new technologies. Lightweight design is considered as one of the most innovative concepts to fulfil environmental, safety and many other objectives at competitive prices. A wide range of design and process parameters needs to be evaluated to achieve numerous goals of production. These goals often stand in conflict with each other. In addition to the variation of the concepts and following the objectives, some limitations such as manufacturing restrictions, financial limits, and deadlines influence the choice of the best combination of variables.
This study introduces a structural optimization tool for assemblies made of sheet metal, e.g. the automobile body, based on parametrization and evaluation of concepts in CAD and CAE. This methodology focuses on those concepts, which leads to the use of the right amount of light and strong material in the right place, instead of substituting the whole structure with the new material.
An adaptive hybrid metaheuristic algorithm is designed to eliminate all factors that would lead to a local minimum instead of global optimum. Finding the global optimum is granted by using some explorative and exploitative search heuristics, which are intelligently organized by a central controller. Reliability, accuracy and the speed of the proposed algorithm are validated via a comparative study with similar algorithms for an academic optimization problem, which shows valuable results.
Since structures might be subject to a wide range of load cases, e.g. static, cyclic, dynamic, temperature-dependent etc., these requirements need to be addressed by a multidisciplinary optimization algorithm. To handle the nonlinear response of objectives and to tackle the time-consuming FEM analyses in crash situations, a surrogate model is implemented in the optimization tool.